IMPC summary stat exploratory data analysis

Load R packages

rm(list=ls())
#library(plyr)
#library(ggplot2)
library(RColorBrewer)
#library(reshape2)
#library(mvtnorm)
#library(corrplot)
library(data.table) #fread
library(knitr)
library(kableExtra)
library(plotly)
library(pheatmap)
library(heatmaply)

color.vec <- brewer.pal(8, "Set1")

Reading the summary data

## load IMPC summary stats 7.0
IMPC.summary.stats.file <- "~/Dropbox/KOMP/results/IMPC_Summary_Stat_Study/R_input/version_7.0/IMPC_ALL_statistical_results.csv"
IMPC.summary.stats <- as.data.frame(fread(IMPC.summary.stats.file, header=TRUE, sep=","))
# Warning in require_bit64(): Some columns are type 'integer64' but
# package bit64 is not installed. Those columns will print as strange
# looking floating point data. There is no need to reload the data. Simply
# install.packages('bit64') to obtain the integer64 print method and print
# the data again.
IMPC.summary.stats$marker_symbol_combined <- paste0(IMPC.summary.stats$marker_symbol,"_",IMPC.summary.stats$zygosity)
IMPC.summary.stats$intermediate_mp_term_name <- NULL
IMPC.summary.stats$intermediate_mp_term_id <- NULL
dim(IMPC.summary.stats)
# [1] 1708110      86
colnames(IMPC.summary.stats)
#  [1] "phenotyping_center"                
#  [2] "intercept_estimate"                
#  [3] "procedure_id"                      
#  [4] "mutant_biological_model_id"        
#  [5] "rotated_residuals_test"            
#  [6] "weight_effect_p_value"             
#  [7] "male_mutant_count"                 
#  [8] "pipeline_stable_key"               
#  [9] "female_ko_effect_p_value"          
# [10] "pipeline_stable_id"                
# [11] "parameter_stable_key"              
# [12] "data_type"                         
# [13] "parameter_stable_id"               
# [14] "interaction_significant"           
# [15] "strain_accession_id"               
# [16] "control_selection_method"          
# [17] "parameter_name"                    
# [18] "allele_name"                       
# [19] "phenotyping_center_id"             
# [20] "weight_effect_stderr_estimate"     
# [21] "weight_effect_parameter_estimate"  
# [22] "procedure_stable_id"               
# [23] "status"                            
# [24] "sex_effect_parameter_estimate"     
# [25] "female_ko_effect_stderr_estimate"  
# [26] "female_percentage_change"          
# [27] "group_2_residuals_normality_test"  
# [28] "marker_accession_id"               
# [29] "mp_term_name"                      
# [30] "group_1_residuals_normality_test"  
# [31] "genotype_effect_p_value"           
# [32] "dependent_variable"                
# [33] "resource_name"                     
# [34] "project_id"                        
# [35] "procedure_name"                    
# [36] "doc_id"                            
# [37] "top_level_mp_term_id"              
# [38] "allele_accession_id"               
# [39] "blups_test"                        
# [40] "null_test_p_value"                 
# [41] "p_value"                           
# [42] "marker_symbol"                     
# [43] "control_biological_model_id"       
# [44] "pipeline_name"                     
# [45] "sex"                               
# [46] "interaction_effect_p_value"        
# [47] "colony_id"                         
# [48] "project_name"                      
# [49] "female_ko_parameter_estimate"      
# [50] "female_mutant_count"               
# [51] "organisation_id"                   
# [52] "external_db_id"                    
# [53] "female_control_count"              
# [54] "db_id"                             
# [55] "male_ko_effect_p_value"            
# [56] "top_level_mp_term_name"            
# [57] "metadata_group"                    
# [58] "sex_effect_stderr_estimate"        
# [59] "zygosity"                          
# [60] "male_percentage_change"            
# [61] "sex_effect_p_value"                
# [62] "mp_term_id"                        
# [63] "male_ko_effect_stderr_estimate"    
# [64] "additional_information"            
# [65] "statistical_method"                
# [66] "_version_"                         
# [67] "intercept_estimate_stderr_estimate"
# [68] "male_control_count"                
# [69] "strain_name"                       
# [70] "classification_tag"                
# [71] "effect_size"                       
# [72] "procedure_stable_key"              
# [73] "allele_symbol"                     
# [74] "resource_id"                       
# [75] "group_2_genotype"                  
# [76] "variance_significant"              
# [77] "pipeline_id"                       
# [78] "group_1_genotype"                  
# [79] "male_ko_parameter_estimate"        
# [80] "genotype_effect_parameter_estimate"
# [81] "categories"                        
# [82] "parameter_id"                      
# [83] "batch_significant"                 
# [84] "genotype_effect_stderr_estimate"   
# [85] "resource_fullname"                 
# [86] "marker_symbol_combined"

First part of summary table

kable(as.data.frame(head(IMPC.summary.stats)), caption = "Head of Table") %>%
  kable_styling(bootstrap_options = c("striped","hover","condensed","responsive"), full_width = F, position="left", font_size = 10)

Phenotyping Centers

pheno.center <- as.data.frame.vector(table(IMPC.summary.stats$phenotyping_center))
colnames(pheno.center) <- "Counts"
kable(pheno.center) %>% 
  kable_styling(bootstrap_options = c("striped","condensed","responsive"), full_width = F)

Phenotyping Procedures

proc.name <- as.data.frame.vector(table(IMPC.summary.stats$procedure_name))
colnames(proc.name) <- "Counts"
kable(proc.name) %>% 
  kable_styling(bootstrap_options = c("striped","condensed","responsive"), full_width = F)

Phenotype Names

pheno.name <- as.data.frame.vector(table(IMPC.summary.stats$parameter_name))
colnames(pheno.name) <- "Counts"
kable(pheno.name) %>% 
  kable_styling(bootstrap_options = c("striped","condensed","responsive"), full_width = F)

ggplot(data=pheno.name, aes(pheno.name$Counts)) + geom_histogram() + labs(x="Phenotype", y="Count")
# Don't know how to automatically pick scale for object of type table. Defaulting to continuous.
# `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Mutant Lines

mutant.lines <- as.data.frame.vector(table(IMPC.summary.stats$marker_symbol_combined))
colnames(mutant.lines) <- "Counts"
kable(mutant.lines) %>% 
  kable_styling(bootstrap_options = c("striped","condensed","responsive"), full_width = F)